# Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

arXiv: https://arxiv.org/abs/1901.11164 · HTML: https://mlsanity.com/paper/1901.11164/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 23 of 23)

- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment](https://mlsanity.com/paper/2303.05725/) (2023)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Isolated Sign Language Recognition based on Tree Structure Skeleton Images](https://mlsanity.com/paper/2304.05403/) (2023)
- [Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production](https://mlsanity.com/paper/2112.05277/) (2021)
- [ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP)](https://mlsanity.com/paper/2201.07899/) (2022)
- [Survey on Hand Gesture Recognition from Visual Input](https://mlsanity.com/paper/2501.11992/) (2025)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation](https://mlsanity.com/paper/2211.00526/) (2022)
- [GestSync: Determining who is speaking without a talking head](https://mlsanity.com/paper/2310.05304/) (2023)
- [Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets](https://mlsanity.com/paper/2403.14534/) (2024)
- [Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions and Skeletal Information](https://mlsanity.com/paper/2106.15989/) (2021)
- [ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language](https://mlsanity.com/paper/2201.02065/) (2022)
- [Combining Efficient and Precise Sign Language Recognition: Good pose estimation library is all you need](https://mlsanity.com/paper/2210.00893/) (2022)
- [Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries](https://mlsanity.com/paper/2301.03769/) (2023)
- [A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognition](https://mlsanity.com/paper/2407.09544/) (2024)
- [SignBart -- New approach with the skeleton sequence for Isolated Sign language Recognition](https://mlsanity.com/paper/2506.21592/) (2025)
- [Graph Neural Network based Child Activity Recognition](https://mlsanity.com/paper/2212.09013/) (2022)
- [New keypoint-based approach for recognising British Sign Language (BSL) from sequences](https://mlsanity.com/paper/2412.09475/) (2024)
- [Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network](https://mlsanity.com/paper/2509.08661/) (2025)
- [Sign language recognition from skeletal data using graph and recurrent neural networks](https://mlsanity.com/paper/2511.05772/) (2025)
- [RoCoISLR: A Romanian Corpus for Isolated Sign Language Recognition](https://mlsanity.com/paper/2511.12767/) (2025)
- [The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognition](https://mlsanity.com/paper/2607.11341/) (2026)

## Abstract (arXiv)

The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the signs dynamics in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies.